Download the dataset
data <- readRDS("C:/Users/enami/Downloads/very_low_birthweight.RDS")
Find and delete all NAs in columns
na_counts <- colSums(is.na(data))
data_nona <- data[, na_counts <= 100]
And now in rows
data_clean <- na.omit(data_nona)
Lets do the numeric variables density plots
numeric_v <- data_clean %>% select(where(is.numeric))
par(mfrow = c(3, 3))
for (var in names(numeric_v)) {
plot(density(numeric_v[[var]], na.rm = TRUE),
main = paste("Плотность для", var),
xlab = var,
ylab = "Плотность")
}
IQR=Q3−Q1 Нижняя:
Q1−1.5⋅IQR Верхняя:
Q3+1.5⋅IQR
remove_outlier <- function(data, var) {
q <- quantile(data[[var]], probs = c(0.25, 0.75), na.rm = TRUE)
iqr <- q[2] - q[1]
lower <- q[1] - 1.5 * iqr
upper <- q[2] + 1.5 * iqr
data %>% filter(data[[var]] >= lower, data[[var]] <= upper)
}
####the function on top is supposed to remove outliers based on IQR
data_nooutlier <- numeric_v
for (var in names(data_nooutlier)) {
data_nooutlier <- remove_outlier(data_nooutlier, var)
}
Lets do categorical transformation
categorical_v <- data_clean %>% select(where(is.character))
categorical_v <- categorical_v %>% mutate(across(everything(), as.factor))
Graph for two numeric..?
data_clean <- data_clean %>%
mutate(dead = as.factor(dead), pneumo = as.factor(pneumo), inout = as.factor(inout))
ggplot(data = data_clean, aes(x = pneumo, fill = inout)) +
geom_bar(position = "dodge", aes(y = ..count..)) +
facet_wrap(~ dead, labeller = labeller(dead = c("0" = "Выжившие", "1" = "Умершие"))) +
theme_minimal() +
labs(
title = "Распределение пневмонии по inout и статусу выживания",
x = "Наличие пневмонии",
y = "Количество случаев",
fill = "In/Out"
)
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Please, dont kill me but i will check normality probably
data_clean %>%
group_by(inout) %>%
summarise(shapiro = list(shapiro_test(lowph))) %>%
unnest(shapiro)
## # A tibble: 2 × 4
## inout variable statistic p.value
## <fct> <chr> <dbl> <dbl>
## 1 born at Duke lowph 0.965 0.00000000758
## 2 transported lowph 0.965 0.0228
test <- wilcox_test(data_clean, lowph ~ inout)
test
## # A tibble: 1 × 7
## .y. group1 group2 n1 n2 statistic p
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl>
## 1 lowph born at Duke transported 448 83 25630. 0.0000000417
data_clean %>%
ggplot(aes(x = inout, y = lowph, color = inout)) +
geom_boxplot() +
stat_compare_means(method = "wilcox.test", label = "p.signif")
rstatixplot
library(rstatix)
ggboxplot(data_clean, x = "inout", y = "lowph",
color = "inout", palette = "jco") +
stat_compare_means(method = "wilcox.test")
Значение lowPH статистически значимо отличаются внутри группы inout. Так
как, группа transported имеет значимо более низкие уровни pH, то и
выживаемость у них будет ожидаться ниже
datacontinuous <- data_clean %>%
select(-c(birth, year, exit)) %>%
select_if(is.numeric)
cormatrix <- cor(datacontinuous, use = "complete.obs")
print(cormatrix)
## hospstay lowph pltct bwt gest
## hospstay 1.000000000 -0.09460363 -0.05993874 -0.2315169 -0.18461328
## lowph -0.094603635 1.00000000 0.26643681 0.3261464 0.37926758
## pltct -0.059938743 0.26643681 1.00000000 0.2598576 0.06545086
## bwt -0.231516924 0.32614640 0.25985757 1.0000000 0.69134598
## gest -0.184613279 0.37926758 0.06545086 0.6913460 1.00000000
## twn 0.005945527 0.03835193 -0.01407561 0.1614821 0.17101963
## apg1 -0.065378517 0.26799978 0.28021129 0.3309873 0.25568063
## vent 0.252473223 -0.58322801 -0.27313969 -0.3825846 -0.41200521
## pda 0.202599688 -0.22489191 -0.22166244 -0.2591969 -0.29283921
## cld 0.385734403 -0.30046333 -0.18031810 -0.4552955 -0.42163729
## twn apg1 vent pda cld
## hospstay 0.005945527 -0.06537852 0.25247322 0.20259969 0.38573440
## lowph 0.038351928 0.26799978 -0.58322801 -0.22489191 -0.30046333
## pltct -0.014075607 0.28021129 -0.27313969 -0.22166244 -0.18031810
## bwt 0.161482147 0.33098733 -0.38258456 -0.25919691 -0.45529548
## gest 0.171019629 0.25568063 -0.41200521 -0.29283921 -0.42163729
## twn 1.000000000 0.06570395 -0.03854605 0.01447667 -0.08853468
## apg1 0.065703952 1.00000000 -0.33681567 -0.19312965 -0.26967928
## vent -0.038546054 -0.33681567 1.00000000 0.35471303 0.48398148
## pda 0.014476666 -0.19312965 0.35471303 1.00000000 0.40233337
## cld -0.088534685 -0.26967928 0.48398148 0.40233337 1.00000000
corrplot(cormatrix, method = "circle", type = "upper", order = "hclust",
col = colorRampPalette(c("darkblue", "white", "darkred"))(200),
tl.col = "black", tl.srt = 45)
heatmap(cormatrix,
col = colorRampPalette(c("blue", "white", "red"))(200),
scale = "none", # не масштабировать данные
margins = c(10, 10))
Иерархическая кластеризация
distmx <- as.dist(1 - cormatrix)
hclust <- hclust(distmx, method = "ward.D2")
plot(hclust, main = "Иерархическая кластеризация",
xlab = "Переменные", sub = "", cex = 0.8)
Тепловая карта и дендрограмма
pheatmap(cormatrix,
color = colorRampPalette(c("darkblue", "ivory", "darkred"))(200),
cluster_rows = hclust,
cluster_cols = hclust,
main = "Тепловая карта и дендрограммы")
PCA Анализ
sapply(datacontinuous, range) #разброс значений перемнных отличается значительно требуется шкалирование!
## hospstay lowph pltct bwt gest twn apg1 vent pda cld
## [1,] -295 6.529999 16 400 23 0 0 0 0 0
## [2,] 797 7.549999 571 1500 36 1 9 1 1 1
pca <- prcomp(datacontinuous, scale. = TRUE)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.8711 1.0815 1.0553 0.92904 0.88809 0.86784 0.8331
## Proportion of Variance 0.3501 0.1170 0.1114 0.08631 0.07887 0.07532 0.0694
## Cumulative Proportion 0.3501 0.4671 0.5784 0.66475 0.74362 0.81893 0.8883
## PC8 PC9 PC10
## Standard deviation 0.70031 0.60398 0.51133
## Proportion of Variance 0.04904 0.03648 0.02615
## Cumulative Proportion 0.93738 0.97385 1.00000
fviz_eig(pca, addlabels = TRUE, ylim = c(0, 100))
Интерпретация - standard deviation показывает разброс данных вокруг
каждой из компонент, где это значение наибольшее (в нашем случае PC1) та
компонента лучше всего и объясняет различие данных.Proportion of
variance тоже показывает сколько наших данных обьясняет компонента, как
и кумулятивная пропорция. В нашем случае наиболее важная компонента -
первая
Biplot
data_dead <- datacontinuous
data_dead$dead <- data_clean$dead
pca <- prcomp(datacontinuous, scale. = TRUE)
fviz_pca_biplot(pca,
geom.ind = "point",
pointshape = 21,
pointsize = 3,
fill.ind = data_dead$dead,
col.var = "black",
gradient.cols = c("darkblue", "cyan", "darkred"),
repel = TRUE,
legend.title = "Dead")
transfer to plotly
interactive biplot
data_clean$id <- seq_len(nrow(data_clean))
dataid <- data_clean %>%
select(-c(birth, year, exit)) %>%
select_if(is.numeric)
dataid$id <- data_clean$id
dataid$dead <- data_clean$dead
pca <- prcomp(dataid %>% select(-c(id, dead)), scale. = TRUE)
pca_2 <- as.data.frame(pca$x)
pca_2$id <- dataid$id
pca_2$dead <- dataid$dead
pca_var <- as.data.frame(pca$rotation)
pca_var$varnames <- rownames(pca_var)
fig <- plot_ly() %>%
# Добавление точек для наблюдений
add_trace(
data = pca_2,
x = ~PC1, y = ~PC2,
type = "scatter",
mode = "markers",
text = ~paste("ID:", id, "Dead:", dead),
hoverinfo = "text",
marker = list(
size = 10,
color = ~dead,
colorscale = "RdBu",
showscale = TRUE
)
) %>%
add_trace(
data = pca_var,
x = c(rep(0, nrow(pca_var)), pca_var$PC1 * 5),
y = c(rep(0, nrow(pca_var)), pca_var$PC2 * 5),
type = "scatter",
mode = "lines+text",
line = list(color = "black"),
text = c(rep("", nrow(pca_var)), pca_var$varnames),
textposition = "top right",
hoverinfo = "text"
) %>%
layout(
title = "PCA Biplot (Interactive)",
xaxis = list(title = "PC1"),
yaxis = list(title = "PC2"),
showlegend = FALSE
)
fig
Мы не выявили причинно-следственной связи между выживаемостью и распределением другим данных, нам все равно нужно проводить дополнительный анализ выживаемости, метод главных компонент поможет нам лишь отобрать те данные, которые с большей вероятностью предскажут dead статус. Dead переменная также принимает только два значения 0 и 1 (нет и да), а для PCA нам нужны непрерывные переменные
UMAP
pca <- prcomp(dataid %>% select(-c(id, dead)), scale. = TRUE)
pca_d <- as.data.frame(pca$x)
pca_d$id <- dataid$id
pca_d$dead <- dataid$dead
umap <- umap(dataid %>% select(-c(id, dead)))
umap_d <- as.data.frame(umap$layout)
umap_d$id <- dataid$id
umap_d$dead <- dataid$dead
pcaplot <- ggplot(pca_d, aes(x = PC1, y = PC2, color = as.factor(dead))) +
geom_point() +
labs(title = "PCA ", color = "Dead") +
theme_minimal()
umapplot <- ggplot(umap_d, aes(x = V1, y = V2, color = as.factor(dead))) +
geom_point() +
labs(title = "UMAP ", color = "Dead") +
theme_minimal()
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
grid.arrange(pcaplot, umapplot, ncol = 2)
UMAP change distance (я не поняла, нужно ли нам опять делать раскраску
по переменной dead, сделала ее в прошлом примере навсякий случай, но
дальше не буду)
umap_res_1 <- umap(dataid %>% select(-c(id, dead)),
n_neighbors = 10, min_dist = 0.1)
umap_data_1 <- as.data.frame(umap_res_1$layout)
umap_plot_1 <- ggplot(umap_data_1, aes(x = V1, y = V2)) +
geom_point(color = "pink") +
labs(title = "UMAP") +
theme_minimal()
umap_plot_1
Результат - дата стала менее структурированной (нет четкого выделения
кластеров, как в прошлых примерах). Я считаю, что это связано с тем что
я уменьшила дистанцию и соседей и алгоритм теперь основывается на
локальных связях между точками и старается их группировать компактно
между собой, при этом мы упускаем глобальную структуру данных
Permutation task
data_clean$bwt_50 <- data_clean$bwt
num_rows <- nrow(data_clean)
num_permuted <- round(num_rows * 0.5)
perm_indices <- sample(1:num_rows, num_permuted)
data_clean$bwt_50[perm_indices] <- sample(data_clean$bwt_50[perm_indices])
data_clean$bwt_100 <- sample(data_clean$bwt)
perform_pca <- function(data, column_name) {
data_numeric <- data %>% select(-c(birth, year, exit, column_name)) %>% select_if(is.numeric)
pca <- prcomp(data_numeric, scale. = TRUE)
pca_result <- summary(pca)
return(pca_result)
}
perform_umap <- function(data, column_name) {
data_numeric <- data %>% select(-c(birth, year, exit, column_name)) %>% select_if(is.numeric)
umap_model <- umap(data_numeric)
return(umap_model$layout)
}
pca_original <- perform_pca(data_clean, 'bwt')
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(column_name)
##
## # Now:
## data %>% select(all_of(column_name))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pca_original
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7859 1.1299 1.08060 1.0206 0.97193 0.9315 0.91101
## Proportion of Variance 0.2658 0.1064 0.09731 0.0868 0.07872 0.0723 0.06916
## Cumulative Proportion 0.2658 0.3722 0.46948 0.5563 0.63500 0.7073 0.77646
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.84315 0.81449 0.69931 0.68132 0.59573
## Proportion of Variance 0.05924 0.05528 0.04075 0.03868 0.02957
## Cumulative Proportion 0.83571 0.89099 0.93174 0.97043 1.00000
umap_original <- perform_umap(data_clean, 'bwt')
umap_original
## [,1] [,2]
## 2 0.08134178 -1.966536179
## 4 1.93980803 -0.046309478
## 5 -0.78792197 3.947475861
## 7 2.84173343 1.100311245
## 10 -1.09816622 4.087933307
## 11 1.75893969 2.135278796
## 13 1.57316124 2.486712557
## 14 -0.71059838 4.110642262
## 15 -3.60873091 -2.302998087
## 16 -1.19219162 4.038703908
## 17 0.32280846 0.845107422
## 19 1.70882817 2.200318458
## 20 2.65620504 1.486608213
## 21 -3.10636731 -3.018667999
## 22 1.55525149 2.323932122
## 23 0.88482424 0.167100610
## 25 1.57228777 0.302639466
## 27 -1.09352000 3.911413993
## 28 -0.26872983 3.968739879
## 29 -3.17837834 -2.729470424
## 30 0.41320223 -1.526142809
## 31 0.14257462 4.012757768
## 32 -0.68084561 3.971331880
## 35 2.28234739 0.479797051
## 36 -3.78949781 -2.113417138
## 40 -0.71464294 3.984270457
## 41 0.15746315 -2.033397980
## 42 1.08758300 0.230774209
## 43 2.72694568 1.424104685
## 45 -3.41858695 -2.462539209
## 46 0.42461445 1.586874795
## 47 -0.46943981 -2.935400251
## 48 1.94437643 -0.084729197
## 50 -2.92595518 -2.787818795
## 51 1.48296831 0.406200707
## 53 0.47061231 0.374515407
## 54 -0.13828922 4.021913591
## 55 1.63459369 0.323319418
## 56 1.73329296 2.195746910
## 57 0.27673771 3.283292737
## 58 0.32449883 -1.860750735
## 59 0.94668629 0.902173757
## 62 -2.82316179 -2.906885404
## 65 0.06531802 3.761128526
## 66 1.62121114 2.261116786
## 67 0.01352182 3.901739048
## 68 0.79991427 0.371959971
## 69 -2.42020772 -2.957471313
## 70 -3.04719138 -3.067416593
## 72 0.18423962 -2.104755555
## 74 2.87172523 0.867179349
## 75 1.41398297 0.546055413
## 76 -1.18302060 3.974698727
## 77 -3.84034400 -2.093087420
## 78 0.55232887 1.955211828
## 80 1.47378103 2.098831438
## 81 2.68453363 0.915036406
## 82 0.09394661 3.239266313
## 83 2.28591904 0.693755399
## 84 -3.14563055 -2.562646268
## 85 0.21652240 -1.042085676
## 86 1.79427149 0.051520783
## 87 1.46887629 2.074180288
## 88 -3.80455403 -2.187913595
## 91 0.08243764 -2.135658848
## 92 0.16051648 -1.623706740
## 94 2.00887581 0.921777512
## 95 -3.01247806 -2.973014705
## 97 -1.08466037 4.007441442
## 98 0.88892340 0.162568054
## 99 2.65771953 1.433147195
## 101 0.93271430 0.828806078
## 102 -2.49786199 -2.961685832
## 103 -2.83125886 -2.863778054
## 104 2.94389638 1.157423487
## 105 2.19680980 0.059766690
## 107 1.52868523 2.338231628
## 108 0.06012182 3.894425318
## 109 1.33025439 0.945850866
## 111 0.28836703 3.290411211
## 114 -3.76393922 -1.942868238
## 115 0.58694776 -1.819891165
## 116 -0.03166899 -2.176174008
## 117 -0.62417555 4.023413212
## 118 1.70200579 0.578433194
## 119 0.63005660 1.075042399
## 120 -2.57483227 -2.865564572
## 121 0.22800217 0.575304602
## 122 0.50535111 -1.961687193
## 124 1.66298763 0.026527564
## 125 -3.15413865 -2.520265257
## 126 0.24762507 3.536646053
## 127 1.02239188 0.697180842
## 128 1.08599307 2.482018173
## 129 2.01309896 0.482226309
## 130 -2.96123173 -3.106520583
## 131 -1.26158119 3.991629569
## 132 0.08279661 0.467670260
## 133 -3.79472654 -2.158355602
## 134 0.75034081 1.267357908
## 137 2.01317935 0.340036538
## 139 -0.77063869 4.170797265
## 140 -0.11048696 3.999906727
## 141 0.29474333 0.384249768
## 143 0.37290667 -1.886177857
## 144 0.17598967 -2.326686368
## 145 -0.76101578 3.860048653
## 146 -3.10183897 -3.029863955
## 148 0.01181231 3.809615676
## 149 -0.41581837 1.427431468
## 150 0.59985434 2.101960641
## 151 0.47645286 -1.718167529
## 152 1.43773100 -0.141800979
## 153 -0.42585575 -0.332970605
## 154 -0.50343170 4.114776142
## 155 2.18652144 0.300169643
## 156 1.03907643 2.446005103
## 157 0.03447071 0.121985299
## 159 2.61197734 1.105051515
## 160 -0.26152948 -0.268353298
## 161 -0.03924953 1.660450399
## 162 -0.96707176 4.102712260
## 163 -0.39902096 1.283701448
## 164 1.10896048 2.627802307
## 165 1.91244738 1.902428909
## 166 -2.65044913 -2.426899694
## 167 -3.09845365 -2.682515390
## 168 1.96011265 0.314512418
## 169 3.26732263 1.135490520
## 170 -0.57131011 -0.381247347
## 172 -0.96654489 3.450760201
## 173 0.07899598 0.411514377
## 174 -0.16228067 3.825892977
## 176 -3.86878010 -2.031669494
## 177 1.76706067 2.051492759
## 179 -0.05615475 -0.575720151
## 180 -2.96119210 -2.677424145
## 182 -3.17670336 -2.587728404
## 183 -3.87508615 -2.056525275
## 184 0.01077409 0.578187462
## 185 -0.99508493 3.351106270
## 186 1.25780530 0.821003071
## 187 -0.43476370 3.773657577
## 188 2.06317325 -0.171808838
## 189 1.26543518 2.560885405
## 190 0.06288651 -2.542704946
## 191 1.84829939 -0.244464058
## 192 -0.58921890 4.225905860
## 193 -2.75430013 3.210909523
## 194 -3.70196031 -1.411678401
## 195 -3.78600417 -1.313642638
## 196 1.04465831 -0.195007742
## 197 -0.68350110 3.611756360
## 198 0.83433612 1.569518555
## 199 -0.44250362 -0.342339245
## 201 2.96477340 0.772056096
## 202 0.80276249 0.749031435
## 203 2.30325659 0.067705374
## 205 0.13270290 -2.287905011
## 206 -3.56161725 -1.165593438
## 207 3.11396102 0.626081313
## 208 0.96732452 2.436624900
## 210 -0.26555014 -1.558263245
## 211 -0.40917459 -0.278290290
## 212 0.56170798 -1.976070611
## 213 -3.83906505 -1.731139974
## 215 -1.14484624 4.027624434
## 216 -0.77901549 1.695437867
## 217 -3.59071331 -1.084080526
## 218 1.41873855 2.587566946
## 219 -3.25657393 -2.158231066
## 220 3.28550230 1.119883918
## 221 -0.15888866 3.843407794
## 222 -4.01914130 -1.033267608
## 223 0.99118717 2.080085683
## 224 1.44768013 -0.233876187
## 225 2.46096639 -0.164702209
## 226 -0.29834984 4.261205006
## 227 -3.30607824 -2.109988300
## 228 0.02399707 -0.593885929
## 229 0.22801516 2.259182087
## 230 -0.19154870 -0.268012225
## 231 3.09582120 0.728186542
## 232 0.15901489 2.943611486
## 233 2.40963745 -0.522124149
## 234 -1.27072299 3.573647077
## 235 1.20011108 1.653242233
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## 405 -4.14799832 -0.489077813
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## 429 -1.34512284 2.506501038
## 430 -3.57801351 -0.015250679
## 431 4.45090774 -0.415048975
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## 434 3.37938395 -0.643833326
## 436 -3.86707861 -0.554506147
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## 438 4.46322311 -0.403818120
## 439 1.97219832 -1.686029466
## 440 -2.60949796 -1.760126752
## 441 0.15905169 -3.428050017
## 442 -1.74231558 2.573389572
## 443 1.10839845 -1.712019267
## 444 -1.11856784 -2.810516439
## 445 4.30303512 0.207893479
## 446 4.77584792 0.563219257
## 449 -1.81460922 1.906355419
## 450 3.26298789 -0.966843292
## 451 -2.77523341 2.575193077
## 452 -1.23145799 2.372023570
## 453 3.62474363 -0.450577424
## 454 -1.44621401 2.483424156
## 455 4.93880077 0.354882641
## 457 3.57303423 -0.591841624
## 458 -2.39572967 2.312615122
## 459 4.52900801 -0.431795140
## 460 -3.37231790 0.135095806
## 461 -2.23180959 2.168385971
## 462 -1.22524124 2.347764153
## 463 -1.22090701 2.139066895
## 464 3.54872544 -0.418419786
## 465 -1.49788372 2.491431781
## 466 -0.06574729 -3.361808651
## 467 -1.32470166 -2.659824967
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## 470 -2.20784689 -0.888054540
## 471 -0.27193251 -3.431796603
## 472 -0.73944698 -1.878525188
## 474 4.16286464 0.142645239
## 475 3.46430845 -0.806125837
## 476 1.09860318 -1.325430790
## 477 0.91542553 -3.174645658
## 478 -1.72386549 -1.326380608
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## 480 4.26102096 0.447586904
## 484 -1.94423072 -1.041546534
## 485 -1.19872178 -1.580901316
## 486 0.73821488 -3.405818765
## 488 2.14313114 -1.965869035
## 490 3.57795942 -0.637533109
## 491 3.55188625 -0.626762060
## 492 2.08413870 -2.165149424
## 493 -1.80425040 -2.225622091
## 494 1.15233543 -3.118107619
## 495 -3.31044791 0.211042807
## 497 -1.16132805 2.334206926
## 498 -2.47062271 -0.061043230
## 499 -1.47299956 1.899363276
## 500 -1.42700848 -2.398018050
## 501 -2.30302648 1.634601074
## 502 -2.68919151 2.399199666
## 503 3.43570587 -1.115676950
## 504 4.58088536 0.288635176
## 505 4.55053223 -0.438908837
## 507 -2.81448390 2.471310506
## 508 -3.55588691 -0.888557710
## 509 4.60845843 -0.550306572
## 511 -3.03598856 0.431228369
## 512 -1.96672381 1.779271859
## 513 2.31201535 -1.963426400
## 514 4.66931022 -0.570203244
## 515 -2.93969935 -1.675717673
## 516 4.60593441 -0.504951885
## 517 2.35930807 -1.986610224
## 518 -3.61114954 0.024864564
## 519 0.96472475 -3.086793959
## 522 -0.80335690 -2.011685984
## 523 -2.24652891 -0.765776133
## 524 -1.33831802 2.490106197
## 525 -2.79179971 0.045713199
## 526 4.02692424 -0.528110091
## 527 1.99429024 -2.388005694
## 528 -3.39093493 1.887372593
## 529 0.49957990 -3.561123998
## 530 2.29587419 -2.039459475
## 531 -2.41476008 -2.148695567
## 532 -1.45358472 2.671285875
## 533 -3.54804961 0.258782447
## 534 0.02576622 -3.387731077
## 535 1.90588624 -2.498836713
## 536 -1.13042066 -1.853586636
## 537 4.20927048 -0.291853688
## 538 -1.77555476 -2.440347555
## 539 3.96927089 -0.683803976
## 541 -1.36394235 -2.404375174
## 542 -1.29293095 -1.937613980
## 543 4.24586536 -0.660464385
## 545 -1.43572720 -2.457812831
## 546 0.95029364 -3.289551063
## 547 4.70593708 -0.537493368
## 548 -2.07546083 1.732019652
## 550 -3.00372072 1.902586920
## 551 -0.83589815 -2.041655919
## 555 -3.41578379 1.981674985
## 558 4.93541645 0.144845495
## 560 -2.32702784 2.204537372
## 561 -1.26973087 -1.792362196
## 563 -2.51412114 2.121752385
## 564 -3.62779765 0.112239069
## 566 -2.95074497 0.177557300
## 568 -3.06747936 -1.574271963
## 569 -2.13578834 1.988738604
## 571 2.35968792 -2.146219094
## 572 0.40762094 -3.645100781
## 573 2.13502045 -2.299632881
## 574 4.00951475 -0.898929518
## 575 2.18482150 -2.298902787
## 578 2.56513264 -1.943286128
## 579 4.77298879 -0.429573300
## 580 0.22066164 -3.612460939
## 581 -2.15415658 1.596906864
## 583 -2.29648459 -0.681985503
## 584 -2.62321531 -1.803706903
## 585 1.15201940 -3.217085684
## 586 5.02064781 0.330544548
## 587 5.05296402 0.065315758
## 588 -1.90912393 -2.579058609
## 590 0.13046222 -3.610176549
## 591 -2.72391575 2.373421361
## 592 2.46529126 -2.103754823
## 593 -2.42508833 2.266182819
## 594 -2.29058201 1.992346460
## 597 -1.96244379 0.928523158
## 599 0.98977677 -3.276426300
## 600 -3.61093400 0.035284204
## 601 5.11472020 0.244957426
## 602 -3.04766609 2.295061260
## 603 -2.13450850 -0.886130536
## 604 0.16377203 -3.644248958
## 605 -2.21017898 1.290865629
## 606 4.81901080 -0.373999474
## 608 2.74292804 -1.946104459
## 609 3.06827893 -1.796444538
## 610 -2.54996641 1.194117464
## 611 -1.37153883 -1.620737156
## 613 -2.51214025 1.441895091
## 615 -2.20041217 0.982255846
## 616 2.79199068 -1.988067226
## 619 -3.53728141 1.379802050
## 620 -3.50849956 0.236791919
## 622 -0.15175376 -3.602075411
## 623 1.65864009 -2.779756410
## 624 -0.91525405 -2.084320105
## 625 5.16456607 0.196806366
## 626 4.75937375 -0.114509800
## 628 -1.91975514 1.394647932
## 629 4.60005479 0.148073777
## 630 -1.17821953 -1.987595769
## 631 5.06591394 0.174524989
## 632 -2.67649195 0.156343641
## 634 -2.34735859 0.683023948
## 636 -1.55236716 -2.256721872
## 638 -3.34472374 1.933952319
## 641 4.76384363 -0.258736530
## 642 0.98997365 -3.262253346
## 643 -3.13512659 2.287265696
## 647 -3.67013157 0.776133282
## 648 4.16916100 -0.810442276
## 649 -1.30030611 -1.592817346
## 650 2.42829186 -2.079432257
## 652 -3.36543299 1.954875326
## 661 0.45898871 -3.624186933
## 662 -0.18321270 -3.536328869
## 664 -2.06415972 1.054689436
## 666 5.12052879 0.141230610
## 667 -2.95207029 0.003462019
## 671 -1.87076897 -1.144009841
pca_50 <- perform_pca(data_clean, 'bwt_50')
pca_50
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.872 1.1180 1.08045 1.03080 0.97266 0.91613 0.87923
## Proportion of Variance 0.292 0.1042 0.09728 0.08855 0.07884 0.06994 0.06442
## Cumulative Proportion 0.292 0.3962 0.49346 0.58200 0.66084 0.73078 0.79520
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.83167 0.8154 0.69811 0.59576 0.50871
## Proportion of Variance 0.05764 0.0554 0.04061 0.02958 0.02157
## Cumulative Proportion 0.85284 0.9082 0.94886 0.97843 1.00000
umap_50 <- perform_umap(data_clean, 'bwt_50')
umap_50
## [,1] [,2]
## 2 -2.805938202 -4.142778707
## 4 -1.659734472 1.843844665
## 5 1.651896028 2.980084361
## 7 -0.865696963 3.598541511
## 10 -1.474080734 2.997435858
## 11 -1.172261043 3.408604910
## 13 0.402465355 3.372756309
## 14 -0.275216531 3.770191186
## 15 2.087477431 -2.024186979
## 16 2.324783927 1.590940910
## 17 -1.697844899 1.796736535
## 19 -1.334591829 3.334948627
## 20 -1.159795953 3.507331337
## 21 1.969863662 -2.293906765
## 22 0.316800868 3.339552276
## 23 2.718916658 1.144829613
## 25 -0.345329321 2.316177128
## 27 2.234690802 1.682644255
## 28 1.838435933 2.733504612
## 29 -2.577656852 -4.422138464
## 30 1.221999817 -1.581800069
## 31 1.742411745 3.215351921
## 32 1.826787028 2.731842378
## 35 -1.588236489 2.464588774
## 36 -2.873986835 -4.142412157
## 40 2.087596848 1.966540846
## 41 -2.834367268 -4.245508804
## 42 -1.109901186 1.858200160
## 43 -1.199322054 3.538077448
## 45 2.386089353 -2.420855177
## 46 -0.069705466 2.528045225
## 47 -2.175416854 -4.469870164
## 48 -1.692575581 1.730015852
## 50 2.669272369 -2.499140629
## 51 -1.585706595 1.949878307
## 53 -0.926664821 1.679694954
## 54 1.857899087 3.076089488
## 55 2.240399349 1.322876638
## 56 -1.490112374 3.496282748
## 57 1.221906735 3.068318638
## 58 -2.910578870 -4.125897565
## 59 -0.576643959 2.389942832
## 62 -2.573042373 -4.396853675
## 65 1.471552305 3.178802854
## 66 1.519447052 3.084232455
## 67 1.945670291 2.614861845
## 68 -1.152230492 1.870442601
## 69 -2.502134460 -4.255889384
## 70 1.924935434 -2.272996532
## 72 -2.953665534 -4.209753494
## 74 -2.841543175 2.572818881
## 75 -1.044015997 2.211991481
## 76 -0.301479360 2.374316564
## 77 2.930576022 -1.726766901
## 78 2.023568178 2.443847940
## 80 -1.445633111 3.150523537
## 81 -1.567114406 3.203977889
## 82 2.111757359 1.575873080
## 83 -0.852748063 2.651762071
## 84 2.929206999 -2.367507817
## 85 0.317453105 -1.847615616
## 86 -1.404280613 1.828975553
## 87 0.001821905 3.032951461
## 88 3.095806919 -1.862155564
## 91 1.832226936 -2.304528466
## 92 -2.519012589 -3.780596541
## 94 1.512120683 2.846206840
## 95 -2.639559490 -4.418329335
## 97 -1.467399069 2.763777447
## 98 -1.715433624 1.782893200
## 99 -0.162496814 3.351465942
## 101 2.189737144 1.461414737
## 102 -2.593618515 -4.335959037
## 103 3.055009714 -2.126097548
## 104 -1.425241686 3.411834852
## 105 -1.647535932 2.009206606
## 107 -0.110607108 3.621017196
## 108 -1.523092139 3.452969616
## 109 -1.657979728 2.580864776
## 111 0.138821254 3.022543052
## 114 2.709224560 -1.519325574
## 115 -2.977076906 -3.734050178
## 116 -2.554252869 -4.096930812
## 117 1.872289194 2.915901618
## 118 -1.271978939 2.431154514
## 119 -0.443275900 2.513022765
## 120 -2.382750843 -4.169651208
## 121 0.925076168 -1.108519113
## 122 -2.941286377 -3.850215018
## 124 -1.537714737 1.861314523
## 125 2.625789364 -2.417337915
## 126 1.414218046 3.153878889
## 127 -1.596711452 2.091771482
## 128 -0.078191569 3.622364303
## 129 -0.624745960 2.418199352
## 130 1.956086905 -2.391810837
## 131 2.555005076 1.563559542
## 132 1.117828369 -1.373971235
## 133 3.047176651 -1.970349401
## 134 -0.377301594 2.599914885
## 137 -0.222275976 2.497123672
## 139 -1.135147944 3.573716649
## 140 1.821514791 3.013928794
## 141 2.534615988 1.187560038
## 143 -2.894074991 -4.254522809
## 144 -2.621313168 -4.411069352
## 145 2.847499452 1.739394806
## 146 2.046410592 -2.466700398
## 148 1.728275935 3.077465640
## 149 -1.839456747 1.758860227
## 150 1.388691693 2.818959999
## 151 2.840003065 -1.105679112
## 152 -1.256403722 1.695297889
## 153 1.384010458 -1.807925300
## 154 -0.567901515 3.333262590
## 155 -1.683981421 2.170271646
## 156 -0.084168051 3.730973655
## 157 0.675593715 -1.516664418
## 159 -0.389857301 3.846073026
## 160 1.532240566 -1.748163022
## 161 0.990631971 2.502488779
## 162 1.734703919 3.128867988
## 163 1.150110382 -1.108867508
## 164 0.556938129 3.232297457
## 165 -0.217972850 3.716048316
## 166 2.449658771 -2.525493740
## 167 2.165680994 -2.456038951
## 168 -1.746648586 2.144653501
## 169 -0.948765232 3.492176149
## 170 1.615072299 -1.885781219
## 172 2.142457590 1.361398665
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## 500 3.755063737 -2.221207166
## 501 0.382514432 0.279591372
## 502 -0.084348751 1.001019140
## 503 3.530085104 0.393808079
## 504 -4.207915418 1.540461754
## 505 -2.253010527 0.500008454
## 507 3.315755289 2.059095522
## 508 3.932286144 -1.831736265
## 509 0.686304174 0.505306907
## 511 -0.755387436 -0.766792630
## 512 0.376696507 0.256131656
## 513 1.716134912 0.130771154
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## 515 3.890385546 -2.133242350
## 516 -3.531011983 1.054004408
## 517 -3.013925345 -0.227311921
## 518 3.490244637 0.027269727
## 519 -1.273724538 -1.873490512
## 522 -1.579701861 -2.002179511
## 523 2.286982615 -0.245120555
## 524 0.685581367 1.031506730
## 525 2.544283290 -0.223461705
## 526 -3.528339777 1.022167826
## 527 -1.638278408 -0.934576835
## 528 3.616410656 0.648976831
## 529 -1.882131089 -2.917179776
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## 531 3.810754716 -2.285057857
## 532 0.116232747 1.076992852
## 533 3.484724876 0.177720905
## 534 -1.413597920 -3.523305322
## 535 -1.707225978 -1.111033717
## 536 -1.315901358 -1.970376385
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## 538 -0.918394980 -3.474191622
## 539 -0.020700236 0.271371238
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## 542 -1.485144341 -2.532474455
## 543 3.778293531 1.179286349
## 545 -0.878452535 -3.285149536
## 546 -1.978645545 -2.071590394
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## 548 3.614114391 0.958650295
## 550 -1.908593677 0.243459231
## 551 -1.741405034 -2.000065723
## 555 3.634610898 0.691347309
## 558 -4.218366859 1.575844201
## 560 0.903312934 0.945743765
## 561 -1.515973796 -2.418101954
## 563 -3.680875765 1.198658132
## 564 3.523782881 -0.078043745
## 566 2.124707484 -0.123975978
## 568 3.894554302 -2.087549584
## 569 0.711839093 0.491796187
## 571 -1.544853966 -0.804411188
## 572 -1.980928685 -2.370716634
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## 574 3.794848587 1.052843223
## 575 -1.379418967 -0.674845497
## 578 -1.374713991 -0.171037469
## 579 -1.410602639 0.234432495
## 580 -0.863635964 -3.042830792
## 581 0.838839092 0.216694356
## 583 2.148982512 -0.331996750
## 584 -0.984684610 -3.494494877
## 585 -2.421121890 -1.296209987
## 586 -4.155416004 1.514029059
## 587 -3.851824510 1.320269420
## 588 -0.971082856 -3.531148925
## 590 -1.338903159 -3.941312306
## 591 -3.840897356 1.339454956
## 592 -1.404921033 -0.354049031
## 593 -0.185967525 0.932972932
## 594 3.785425535 1.158577729
## 597 -0.997156785 -0.355397369
## 599 -2.161236294 -1.655566701
## 600 3.743714397 -0.737965758
## 601 3.617450072 1.480372846
## 602 -3.787531524 1.358865811
## 603 -2.032468333 -2.040552432
## 604 -1.364068491 -3.976463752
## 605 1.067236731 0.119960265
## 606 -1.954972299 0.347197865
## 608 0.082625802 -0.052679329
## 609 -1.138782051 -0.442852376
## 610 0.042189668 -0.018195522
## 611 -1.453414244 -2.538131046
## 613 1.209398399 0.079788493
## 615 1.223365877 -0.027132653
## 616 -1.630603790 -0.773124760
## 619 3.818839069 1.074839649
## 620 3.565894234 0.044563351
## 622 -1.244353807 -3.909903209
## 623 -1.857246127 -1.458312820
## 624 -1.897906920 -2.197360557
## 625 -4.297914749 1.471645260
## 626 0.828812902 0.784727774
## 628 -1.483648147 -0.006400111
## 629 -4.308595342 1.485468815
## 630 -1.677789078 -2.410925904
## 631 -4.292162521 1.481493771
## 632 -1.573767427 -1.584581826
## 634 -1.034269921 -0.515924168
## 636 -0.906312462 -3.462935749
## 638 3.722137975 0.860634742
## 641 0.692164409 0.495783902
## 642 -2.182223440 -1.887052888
## 643 3.634396236 1.242163453
## 647 3.577877550 0.535062918
## 648 0.596802660 0.201013541
## 649 -1.937503310 -1.501323376
## 650 -1.230284202 -0.533738996
## 652 3.730493291 0.842394866
## 661 -1.704134466 -2.535220735
## 662 -1.192556453 -3.786599422
## 664 1.710865587 0.039829904
## 666 -4.295470050 1.466734625
## 667 3.374602342 -0.594264695
## 671 -1.296840102 -2.488335138
pca_100 <- perform_pca(data_clean, 'bwt_100')
pca_100
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.9254 1.1503 1.07384 1.02810 0.95159 0.92141 0.84454
## Proportion of Variance 0.3089 0.1103 0.09609 0.08808 0.07546 0.07075 0.05944
## Cumulative Proportion 0.3089 0.4192 0.51530 0.60339 0.67884 0.74959 0.80903
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.81575 0.73003 0.69914 0.59720 0.49779
## Proportion of Variance 0.05545 0.04441 0.04073 0.02972 0.02065
## Cumulative Proportion 0.86449 0.90890 0.94963 0.97935 1.00000
umap_100 <- perform_umap(data_clean, 'bwt_100')
umap_100
## [,1] [,2]
## 2 -0.214306448 -3.947785635
## 4 -0.705589452 -4.441633588
## 5 2.065682762 3.665941153
## 7 -0.021561143 -3.656894425
## 10 0.475438934 1.000447931
## 11 0.860888101 -2.341927967
## 13 2.191097786 -0.998849987
## 14 0.800645085 1.288005869
## 15 2.148079283 2.464043331
## 16 2.388010149 4.037636992
## 17 0.360129017 0.123260007
## 19 0.592838348 -2.323767641
## 20 -0.083525761 -3.669438398
## 21 2.034261590 2.641907666
## 22 2.254217934 -1.014397878
## 23 3.965507992 -0.263656663
## 25 1.071496974 -3.055047031
## 27 2.437017582 3.871807913
## 28 2.306989765 3.980654619
## 29 1.049063783 -1.610519849
## 30 1.031054648 -3.130462581
## 31 2.116806497 2.596123450
## 32 2.274015447 4.019923087
## 35 -0.582856370 -4.193875425
## 36 0.538307397 0.872321928
## 40 2.477104410 3.866829595
## 41 -0.636625543 -4.281986894
## 42 0.780062270 -2.930520322
## 43 -0.160630950 -3.757898327
## 45 2.063650452 2.462526870
## 46 2.383588416 -0.775852878
## 47 -0.811333702 -4.470322275
## 48 -0.824132057 -4.473601044
## 50 2.222707652 -0.953474177
## 51 0.032850405 -3.102781153
## 53 1.240398192 -2.073294040
## 54 2.082354946 2.882833699
## 55 3.893814086 -0.618568384
## 56 0.575673704 -2.250553234
## 57 2.078373460 2.395926401
## 58 -0.178980507 -3.817869531
## 59 1.741721099 -1.752870193
## 62 0.896596237 -1.991448422
## 65 2.026821970 2.480630425
## 66 2.727159075 -0.731749520
## 67 2.433603374 3.781573795
## 68 0.896515036 -2.339736776
## 69 0.932694135 -2.641134668
## 70 2.328699755 -0.788689255
## 72 -0.653637313 -4.276119200
## 74 -1.254082147 -3.731624036
## 75 0.872144735 -2.603486928
## 76 0.895981949 1.569625441
## 77 2.246869273 3.866366088
## 78 3.793191982 -0.087809291
## 80 0.811920798 -2.209387406
## 81 -0.644857587 -4.387363115
## 82 2.130480208 2.505598244
## 83 0.480289398 -3.259280397
## 84 2.289336313 3.578949442
## 85 0.885933662 -2.645213337
## 86 -0.258930995 -3.881546405
## 87 1.749771654 -1.679183056
## 88 2.156109069 4.071752665
## 91 0.938470285 -3.157549693
## 92 0.868277029 -2.615310668
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ggplot(data.frame(PC1 = pca_original$x[,1], PC2 = pca_original$x[,2]), aes(x = PC1, y = PC2)) +
geom_point() + ggtitle("PCA - O")
ggplot(data.frame(PC1 = pca_50$x[,1], PC2 = pca_50$x[,2]), aes(x = PC1, y = PC2)) +
geom_point() + ggtitle("PCA - 50% ")
ggplot(data.frame(PC1 = pca_100$x[,1], PC2 = pca_100$x[,2]), aes(x = PC1, y = PC2)) +
geom_point() + ggtitle("PCA - 100% ")
ggplot(data.frame(UMAP1 = umap_original[,1], UMAP2 = umap_original[,2]), aes(x = UMAP1, y = UMAP2)) +
geom_point() + ggtitle("UMAP - Orig")
ggplot(data.frame(UMAP1 = umap_50[,1], UMAP2 = umap_50[,2]), aes(x = UMAP1, y = UMAP2)) +
geom_point() + ggtitle("UMAP - 50% ")
ggplot(data.frame(UMAP1 = umap_100[,1], UMAP2 = umap_100[,2]), aes(x = UMAP1, y = UMAP2)) +
geom_point() + ggtitle("UMAP - 100% ")
Честно говоря я не понимаю, что происходит, по визуализации как будто
кумулятивная не должна меняться, просто новые кластеры появляются, но
общий результат должен быть похожим
Если вам не трудно, могли бы вы в фидбеке обьяснить, как правильно интерпретировать и как делать последние два пункта :) Я сдаюсь…